Meta-learning for weighted Voting-based ensemble using neural networks

• Grant number: 20/06767-5
• Support Opportunities: Scholarships in Brazil - Scientific Initiation
• Start date: August 01, 2020
• End date: July 31, 2021
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computing Methodologies and Techniques
• Principal Investigator: Diego Furtado Silva
• Grantee: Claudia Rincon Sanches
• Host Institution: Centro de Ciências Exatas e de Tecnologia (CCET). Universidade Federal de São Carlos (UFSCAR). São Carlos , SP, Brazil

Abstract

Machine Learning (ML) has been widely used to transform data into knowledge and, thus, assist decision making.Due to the vast diversity of uses of ML, there is not a single algorithm that is the most effective in all applicationdomains. Therefore, model combination techniques (ensemble) are a widely used option in this scenario. Anexample of this approach is the recent NN-Stacking regression algorithm, which uses an artificial neural networkto estimate weights for different ML algorithms to combine them using their weighted average. This work aims toexpand the qualities of NN-Stacking, proposing two variations for the weight estimator, in addition to itsadaptation for the classification task. (AU)